AI/ML Engineer – AWS Trainium

Tips: Provide a summary of the role, what success in the position looks like, and how this role fits into the organization overall. We are hiring an experienced AWS Trainium / AI Infrastructure Engineer to support large-scale AI/ML infrastructure and distributed model training workloads in AWS. Role: AWS Trainium / AI Infrastructure Engineer Location: USA – Remote Start Date: Immediate Background Check: Mandatory

Key Responsibilities

Design, deploy, and support scalable AI/ML infrastructure on AWS. Build and optimize distributed training and inference workloads using AWS Trainium/Inferentia. Develop and optimize AI workloads using the AWS Neuron SDK. Support LLM, Generative AI, and large-scale machine learning workloads. Deploy AI workloads using Amazon EKS/Kubernetes, Docker, Python, and PyTorch. Benchmark and optimize AI model performance across GPU and AWS AI accelerator environments. Support migration of GPU-based workloads to AWS Trainium/Inferentia. Collaborate with ML engineers and infrastructure teams to improve scalability, reliability, and performance. Required Skills5+ years of experience in Cloud, Data, AI Infrastructure, or a related field. Hands-on experience with AWS Trainium, Inferentia, EC2 Trn instances, and AWS Neuron SDK. Strong knowledge of LLMs, Generative AI, distributed training, and AI/ML infrastructure. Experience with AWS, EKS/Kubernetes, Docker, Python, and PyTorch. Experience supporting large-scale AI/ML training workloads in AWS. Strong understanding of AI performance optimization, benchmarking, and cloud-native architectures. Experience migrating or optimizing GPU-based AI workloads for AWS accelerators is highly preferred. Interested candidates: Please share your updated resume along with your location, work authorization, and availability.